Disclaimer

Course content

The program

Final exercise

There will be a final graded exercise (pass / fail 0.5 ECT).

We will explain the exercise at the end of today and you can work on it tomorrow morning.

The deadline for handing in the exercise is the 10th of February.

Why learn R?

• Reproducibility of your results
• Free software (unlike stata or SPSS)
• A lot of resources available
• Potentially useful in further career

A word on coding with ChatGTP

• Very helpful!
• Be careful
• Ask for “simple solutions”
• Ask to explain code step-by-step
• Use for debugging your code
• Unfortunately, we still need to learn the basics…

Installing R and RStudio

Did you all manage to install R and RStudio?

Difference R and RStudio

Opening RStudio

Simple calculator

1 + 1 

[1] 2

Using a script

Code and comments

“#” can be use to add text and comments

# Use "#" to add text and explanations to your code 

Keyboard shortcuts

• Run the current line or selection: Ctrl + Enter / Command + Enter
• Run all code in the script: Ctrl + Alt + R / Command + shift + Enter
• Interrupt running code: Esc
• Comment/uncomment lines: Ctrl + Shift + C / Command + shift + C

Saving “objects”

a = 1 
b = 2
c = a + b

# can use = or "<-"

c <- a + b 

c

[1] 3

Why could this be helpful?

Example “objects”

data = c(1,13,2,1,43,53,1,2,34,54,2,4,6,23)

cutoff = 10 

data[data>cutoff]

[1] 13 43 53 34 54 23

Using “functions”

Using “functions”

Input(s) are called “arguments”

sum(1, 2)

[1] 3

sum(a , b)

[1] 3

Getting help with functions

To find out what arguments the function requires

?sum

Vector

c combines its arguments

c(1,2,3)

[1] 1 2 3

c(a, b)

[1] 1 2

Vector

How to access an element in the vector

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

a_vector[4]

[1] 3.11

Array/matrix

matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )

     [,1] [,2]
[1,]    1    5
[2,]    2    6
[3,]    3    7
[4,]    4    8

Array/matrix

How to access an element from a matrix

a_matrix = matrix(data = c(1,2,3,4,5,6,7,8), nrow = 4 )

a_matrix[3,2] # first row, then column

[1] 7

Using functions – Exercise 1

Can you calculate the following for “a_vector”?

• Mean
• Standard deviation
• Maximal value
• Minimal value
• Length of the vector

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

Using functions – Exercise 1 - Solution

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

mean(a_vector)

[1] 4.02425

sd(a_vector)

[1] 1.811263

max(a_vector)

[1] 6.21

min(a_vector)

[1] 1.23

length(a_vector)

[1] 8

Get help for functions – Exercise 2

Can you figure out what you can do with the following functions?

• seq()
• rep()

Get help for functions – Exercise 2 - Solution

seq(1,10)

 [1]  1  2  3  4  5  6  7  8  9 10

seq(1,10, by = 2)

[1] 1 3 5 7 9

rep(0,100)

  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Classes and types

Numeric and character

a_vector = c(1.23, 2.34, 6.21, 3.11, 3.412, 4.32, 5.922, 5.65)

class(a_vector)

[1] "numeric"

b_vector = c("something", "something else", "another thing", "completely differnt")

class(b_vector)

[1] "character"

Classes and types

Logical

c_vector = c(F, T, T,T, F, T, F, T)

class(c_vector)

[1] "logical"

Classes and types

factor

gender <- factor(c("Male", "Female", "Female", "Male"))

class(gender)

[1] "factor"

Combining different types of vectors

Lists

my_list <- list(name = "Alice", age = 25, scores = c(90, 85, 88))

print(my_list)

$name
[1] "Alice"

$age
[1] 25

$scores
[1] 90 85 88

Why do we care about classes and types?

• Helpful for plotting –> we come back to this later
• In functions the class/type of the several input variables are often pre-defined

#sum(c("Kees", "Klaas", "Jan"))

Data frame

# Create a data frame
df <- data.frame(x = 1:3, y = c("a", "b", "c"))

# Printing
print(df)

  x y
1 1 a
2 2 b
3 3 c

# use one of the inbuilt data frames
cars

   speed dist
1      4    2
2      4   10
3      7    4
4      7   22
5      8   16
6      9   10
7     10   18
8     10   26
9     10   34
10    11   17
11    11   28
12    12   14
13    12   20
14    12   24
15    12   28
16    13   26
17    13   34
18    13   34
19    13   46
20    14   26
21    14   36
22    14   60
23    14   80
24    15   20
25    15   26
26    15   54
27    16   32
28    16   40
29    17   32
30    17   40
31    17   50
32    18   42
33    18   56
34    18   76
35    18   84
36    19   36
37    19   46
38    19   68
39    20   32
40    20   48
41    20   52
42    20   56
43    20   64
44    22   66
45    23   54
46    24   70
47    24   92
48    24   93
49    24  120
50    25   85

Tibble

# Create a tibble
library(tibble)
tb <- tibble(x = 1:3, y = c("a", "b", "c"))

print(tb)

# A tibble: 3 × 2
      x y    
  <int> <chr>
1     1 a    
2     2 b    
3     3 c    

Introduction to simple plots

plot(cars$speed, cars$dist)

Introduction to simple plots

hist(cars$speed)

Plot() – Exercise 3

• create a point plot for the sepal length against the sepal width of the iris data
• color the points red
• change x and y labeling
• add a title
• change the type of points

iris

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
3            4.7         3.2          1.3         0.2     setosa
4            4.6         3.1          1.5         0.2     setosa
5            5.0         3.6          1.4         0.2     setosa
6            5.4         3.9          1.7         0.4     setosa
7            4.6         3.4          1.4         0.3     setosa
8            5.0         3.4          1.5         0.2     setosa
9            4.4         2.9          1.4         0.2     setosa
10           4.9         3.1          1.5         0.1     setosa
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica

?plot 

Help on topic 'plot' was found in the following packages:

  Package               Library
  graphics              /Library/Frameworks/R.framework/Versions/4.2/Resources/library
  base                  /Library/Frameworks/R.framework/Resources/library


Using the first match ...

plot() – Exercise 3 – solution

• create a point plot for the sepal length against the sepal width of the iris data
• color the points red
• change x and y labeling
• add a title
• change the type of points

plot(iris$Sepal.Length, iris$Sepal.Width, 
     type = "p", # plot points ("l" would give a line)
     col = "red", # color the points red
     xlab = "length", # change the x label
     ylab = "width", # change the y label 
     main = "Simple example plot", # add tittle
     pch = 4) # change the type of points 

Packages

Get access to specific set of functions

#install.packages("tidyverse")
library(tidyverse)

run “library()” every time you want to use any function from this package

Help for packages

• Using the “Help” rider in the Output pane and typing the package into the search field, which will provide you with a brief description

• Search for package details online in CRAN

• Getting the help documentation of the package, which lists all functions and their description

help(package = "tidyverse")

Exercise 4: Install & load package

• Install the package “pacman”

• open the help page for “pacman”

Exercise 4: Install & load package – solution

You can use pacman function “p_load” to load multiple packages at the same time.

# Install package
install.packages("pacman")

# Load package "pacman"
library(pacman)

# get help 
help(package = "pacman")

# Load all packages required for this class
pacman::p_load(tidyverse, haven, table1, readxl, writexl, labelled, summarytools) 

Goal of the analysis cookbook

• Import and inspect data in R

• Learn the basics of data cleaning and wrangling using tidyverse

• Implement basic operations and summaries in R

• Gain hands-on experience through exercises

Example data

• Data on Covid vaccinations from Basel

• National survey on health and nutrition in the US (https://wwwn.cdc.gov/nchs/nhanes/)

Before downloading data, we organize our directory

Set-up R environment

Essential steps to set-up your R-environment include

• Set directories

• Load required packages

Directory structure

• 01_oridata: Here you store all original files. DO NOT ALTER THIS DATA!

• 02_data: Altered data.

• 03_code: Here you should store all your R-script / markdown / qmd-files etc. In this class, we only work with one script.

• 04_output: Here you store all your output files (e.g., tables, figures).

Use a folder structure, not your desktop!

Set a working directory

• Select a folder on your computer where you will set up the mentioned folder structure.
• In R, set the working directory to this folder

getwd() 

setwd("/Users/jb22m516/Documents/GitHub/getting_started_with_R/")

Set directories

• Copy the code below into your R script.

• Replace “d_proj” with the path to your folder.

• Run the code + check if result is correct.

• Save the script with a name of your choice in the folder “03_script”.

# Set the project root directory
d_proj <- "/Users/jb22m516/Documents/GitHub/getting_started_with_R/lesson_material/exercises"

# Basic folders
# Directory for original data
d_oridata <- file.path(d_proj, "01_oridata")
# Directory for edited data
d_data <- file.path(d_proj, "02_data")
# Directory for R scripts
d_code <- file.path(d_proj, "03_code")
# Directory for R output
d_output <- file.path(d_proj, "04_output")


## This code below creates the folders in case you haven't set them up yet
# Create a vector with all directories
dirs <- c(d_oridata, d_data, d_code, d_output)

# Loop through the directories and create them if they don't exist
for (dir in dirs) {
  if (!dir.exists(dir)) {
    dir.create(dir, recursive = TRUE)
  }
}

Load library for reading excel files

library(readxl)

Download data

download “nhanes_for_R.xlsx” and save it to your folder “01_oridata”

Load data in R

nhanes <- read_excel( file.path(d_proj, "/01_oridata/nhanes_for_R.xlsx") ) 

Options for read_excel()

You can specify several options within read_excel()

# sheet: Specify the sheet name or number.
nhanes <- read_excel(file.path(d_data, "nhanes_for_R.xlsx"), sheet = 1)

# range: Import a specific range of cells.
nhanes <- read_excel(file.path(d_data, "nhanes_for_R.xlsx"), range = "A1:D100")

# col_names: Specify if the first row contains column names.
# TRUE (default): First row is used as column names.
# FALSE: R assigns default column names (X1, X2, etc.).
nhanes <- read_excel(file.path(d_data, "nhanes_for_R.xlsx"), col_names = FALSE)

# skip: Skip the first n rows
nhanes <- read_excel(file.path(d_data, "nhanes_for_R.xlsx"), skip = 3)

# na: Define missing values.
nhanes <- read_excel(file.path(d_data, "nhanes_for_R.xlsx"), na = c("NA", "99"))

Alternative functions for loading data

There are other packages and functions for importing other data formats. Most common are:

• csv-files: read.csv() (Base R) or read_csv() (readr)

• STATA, SAS, SPSS (haven): read_dta (Stata files), read_sav() (SPSS files), read_sas (SAS files)

Exercise 5: Import data

1. Download the data “BMX_J.xpt” from GitHub and put it into your “01_oridata” folder. This is the original data file from the NHANES dataset that contains all body measures (e.g., height, weight).

2. Find out which package you need to import an xpt-file.

3. Import the file in R and assign it to the object “nhanes_body”.

Exercise 5: Import data – solution

1. Download the data “BMX_J.xpt” from GitHub and put it into your “01_oridata” folder. This is the original data file from the NHANES dataset that contains all body measures (e.g., height, weight).

2. Find out which package you need to import an xpt-file.

3. Import the file in R and assign it to the object “nhanes_body”.

library(haven)

nhanes_body <- read_xpt(file.path(d_oridata, "BMX_J.xpt"))

Inspecting the data

Dimensions: Check number of rows and columns

# Dimensions (rows and columns)
dim(nhanes)

[1] 9254   14

# Number of rows only
nrow(nhanes)

[1] 9254

# Number of columns only
ncol(nhanes)

[1] 14

Inspecting the data

Column names: List the names of all variables

colnames(nhanes)

 [1] "SEQN"     "RIAGENDR" "RIDAGEYR" "RIDRETH1" "DMDEDUC2" "INDHHIN2"
 [7] "BMXWT"    "BMXHT"    "BPXSY1"   "BPXDI1"   "BPXPLS"   "SMQ020"  
[13] "SMQ040"   "SMQ900"  

Inspecting the data

Data structure: Provides overview of dataset structure, including variable types and the first few observations

# option 1
str(nhanes)

tibble [9,254 × 14] (S3: tbl_df/tbl/data.frame)
 $ SEQN    : num [1:9254] 93703 93704 93705 93706 93707 ...
 $ RIAGENDR: num [1:9254] 2 1 2 1 1 2 2 2 1 1 ...
 $ RIDAGEYR: num [1:9254] 2 2 66 18 13 66 75 0 56 18 ...
 $ RIDRETH1: num [1:9254] 5 3 4 5 5 5 4 3 5 1 ...
 $ DMDEDUC2: num [1:9254] NA NA 2 NA NA 1 4 NA 5 NA ...
 $ INDHHIN2: num [1:9254] 15 15 3 NA 10 6 2 15 15 4 ...
 $ BMXWT   : num [1:9254] 13.7 13.9 79.5 66.3 45.4 53.5 88.8 10.2 62.1 58.9 ...
 $ BMXHT   : num [1:9254] 88.6 94.2 158.3 175.7 158.4 ...
 $ BPXSY1  : num [1:9254] NA NA NA 112 128 NA 120 NA 108 112 ...
 $ BPXDI1  : num [1:9254] NA NA NA 74 38 NA 66 NA 68 68 ...
 $ BPXPLS  : num [1:9254] NA NA 52 82 100 68 74 NA 62 68 ...
 $ SMQ020  : num [1:9254] NA NA 1 2 NA 2 1 NA 2 1 ...
 $ SMQ040  : num [1:9254] NA NA 3 NA NA NA 1 NA NA 2 ...
 $ SMQ900  : num [1:9254] NA NA 2 2 NA 2 1 NA 2 1 ...

# option 2
glimpse(nhanes)

Rows: 9,254
Columns: 14
$ SEQN     <dbl> 93703, 93704, 93705, 93706, 93707, 93708, 93709, 93710, 93711…
$ RIAGENDR <dbl> 2, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1…
$ RIDAGEYR <dbl> 2, 2, 66, 18, 13, 66, 75, 0, 56, 18, 67, 54, 71, 61, 22, 45, …
$ RIDRETH1 <dbl> 5, 3, 4, 5, 5, 5, 4, 3, 5, 1, 3, 4, 5, 5, 3, 4, 3, 4, 1, 3, 3…
$ DMDEDUC2 <dbl> NA, NA, 2, NA, NA, 1, 4, NA, 5, NA, 3, 4, 3, 5, 3, 3, NA, NA,…
$ INDHHIN2 <dbl> 15, 15, 3, NA, 10, 6, 2, 15, 15, 4, 6, 7, 8, 15, NA, 10, 14, …
$ BMXWT    <dbl> 13.7, 13.9, 79.5, 66.3, 45.4, 53.5, 88.8, 10.2, 62.1, 58.9, 7…
$ BMXHT    <dbl> 88.6, 94.2, 158.3, 175.7, 158.4, 150.2, 151.1, NA, 170.6, 172…
$ BPXSY1   <dbl> NA, NA, NA, 112, 128, NA, 120, NA, 108, 112, 104, NA, 112, 12…
$ BPXDI1   <dbl> NA, NA, NA, 74, 38, NA, 66, NA, 68, 68, 70, NA, 60, 72, 62, 8…
$ BPXPLS   <dbl> NA, NA, 52, 82, 100, 68, 74, NA, 62, 68, 90, 90, 66, 58, 60, …
$ SMQ020   <dbl> NA, NA, 1, 2, NA, 2, 1, NA, 2, 1, 1, 1, 1, 1, 1, 2, NA, NA, 2…
$ SMQ040   <dbl> NA, NA, 3, NA, NA, NA, 1, NA, NA, 2, 1, 3, 1, 3, 1, NA, NA, N…
$ SMQ900   <dbl> NA, NA, 2, 2, NA, 2, 1, NA, 2, 1, 2, 2, 1, 2, 1, 2, NA, NA, 2…

# option 3: This will open the dataset in a separate window with the first couple of hundered of rows and
view(nhanes)

Inspecting the data

Quick summary of each variable: Gives you summary statistics for each column, including missings

summary(nhanes)

      SEQN           RIAGENDR        RIDAGEYR        RIDRETH1    
 Min.   : 93703   Min.   :1.000   Min.   : 0.00   Min.   :1.000  
 1st Qu.: 96016   1st Qu.:1.000   1st Qu.:11.00   1st Qu.:3.000  
 Median : 98330   Median :2.000   Median :31.00   Median :3.000  
 Mean   : 98330   Mean   :1.508   Mean   :34.33   Mean   :3.234  
 3rd Qu.:100643   3rd Qu.:2.000   3rd Qu.:58.00   3rd Qu.:4.000  
 Max.   :102956   Max.   :2.000   Max.   :80.00   Max.   :5.000  
                                                                 
    DMDEDUC2        INDHHIN2        BMXWT            BMXHT      
 Min.   :1.000   Min.   : 1.0   Min.   :  3.20   Min.   : 78.3  
 1st Qu.:3.000   1st Qu.: 6.0   1st Qu.: 43.10   1st Qu.:151.4  
 Median :4.000   Median : 8.0   Median : 67.75   Median :161.9  
 Mean   :3.526   Mean   :12.5   Mean   : 65.14   Mean   :156.6  
 3rd Qu.:4.000   3rd Qu.:14.0   3rd Qu.: 85.60   3rd Qu.:171.2  
 Max.   :9.000   Max.   :99.0   Max.   :242.60   Max.   :197.7  
 NA's   :3685    NA's   :491    NA's   :674      NA's   :1238   
     BPXSY1          BPXDI1           BPXPLS           SMQ020     
 Min.   : 72.0   Min.   :  0.00   Min.   : 34.00   Min.   :1.000  
 1st Qu.:106.0   1st Qu.: 60.00   1st Qu.: 66.00   1st Qu.:1.000  
 Median :118.0   Median : 70.00   Median : 72.00   Median :2.000  
 Mean   :121.3   Mean   : 67.84   Mean   : 73.75   Mean   :1.597  
 3rd Qu.:132.0   3rd Qu.: 76.00   3rd Qu.: 82.00   3rd Qu.:2.000  
 Max.   :228.0   Max.   :136.00   Max.   :136.00   Max.   :2.000  
 NA's   :2952    NA's   :2952     NA's   :2512     NA's   :3398   
     SMQ040          SMQ900     
 Min.   :1.000   Min.   :1.000  
 1st Qu.:1.000   1st Qu.:2.000  
 Median :3.000   Median :2.000  
 Mean   :2.226   Mean   :1.805  
 3rd Qu.:3.000   3rd Qu.:2.000  
 Max.   :3.000   Max.   :9.000  
 NA's   :6895    NA's   :3398   

Inspecting the data

Data preview: View the first view or last few rows of your dataset

# first 6 rows
head(nhanes)

# A tibble: 6 × 14
   SEQN RIAGENDR RIDAGEYR RIDRETH1 DMDEDUC2 INDHHIN2 BMXWT BMXHT BPXSY1 BPXDI1
  <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <dbl> <dbl>  <dbl>  <dbl>
1 93703        2        2        5       NA       15  13.7  88.6     NA     NA
2 93704        1        2        3       NA       15  13.9  94.2     NA     NA
3 93705        2       66        4        2        3  79.5 158.      NA     NA
4 93706        1       18        5       NA       NA  66.3 176.     112     74
5 93707        1       13        5       NA       10  45.4 158.     128     38
6 93708        2       66        5        1        6  53.5 150.      NA     NA
# ℹ 4 more variables: BPXPLS <dbl>, SMQ020 <dbl>, SMQ040 <dbl>, SMQ900 <dbl>

# last 6 rows
tail(nhanes)

# A tibble: 6 × 14
    SEQN RIAGENDR RIDAGEYR RIDRETH1 DMDEDUC2 INDHHIN2 BMXWT BMXHT BPXSY1 BPXDI1
   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <dbl> <dbl>  <dbl>  <dbl>
1 102951        1        4        3       NA       10  23.8  109.     NA     NA
2 102952        2       70        5        3        4  49    156.    136     74
3 102953        1       42        1        3       12  97.4  165.    124     76
4 102954        2       41        4        5       10  69.1  163.    116     66
5 102955        2       14        4       NA        9 112.   157.    114     62
6 102956        1       38        3        4        7 112.   176.    150     98
# ℹ 4 more variables: BPXPLS <dbl>, SMQ020 <dbl>, SMQ040 <dbl>, SMQ900 <dbl>

# You can append this argument to specify the first/last number of rows you want to see:
head(nhanes, n = 10)

# A tibble: 10 × 14
    SEQN RIAGENDR RIDAGEYR RIDRETH1 DMDEDUC2 INDHHIN2 BMXWT BMXHT BPXSY1 BPXDI1
   <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl> <dbl> <dbl>  <dbl>  <dbl>
 1 93703        2        2        5       NA       15  13.7  88.6     NA     NA
 2 93704        1        2        3       NA       15  13.9  94.2     NA     NA
 3 93705        2       66        4        2        3  79.5 158.      NA     NA
 4 93706        1       18        5       NA       NA  66.3 176.     112     74
 5 93707        1       13        5       NA       10  45.4 158.     128     38
 6 93708        2       66        5        1        6  53.5 150.      NA     NA
 7 93709        2       75        4        4        2  88.8 151.     120     66
 8 93710        2        0        3       NA       15  10.2  NA       NA     NA
 9 93711        1       56        5        5       15  62.1 171.     108     68
10 93712        1       18        1       NA        4  58.9 173.     112     68
# ℹ 4 more variables: BPXPLS <dbl>, SMQ020 <dbl>, SMQ040 <dbl>, SMQ900 <dbl>

Inspecting the data

Check class / variable type of your dataset / columns

# Class of the entire dataset
class(nhanes)

[1] "tbl_df"     "tbl"        "data.frame"

# Class of a specific column
class(nhanes$RIAGENDR)

[1] "numeric"

# Data types of all columns
sapply(nhanes, class)

     SEQN  RIAGENDR  RIDAGEYR  RIDRETH1  DMDEDUC2  INDHHIN2     BMXWT     BMXHT 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
   BPXSY1    BPXDI1    BPXPLS    SMQ020    SMQ040    SMQ900 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 

Exercise 6: Checking data structure of a dataset

Download the data Covid19_vaccines_Basel. This dataset contains information about the number of Covid-19 vaccinations in the Canton Basel-Citiy between 2 January 2021 and July 1 2023.

1. Import the excel file Covid_19_vaccines_Basel.xlsx into R and assign it to the object “covid”.

2. How many columns does the dataset have?

3. How many rows?

4. Get an overview/summary of your data.

5. What is the median number of vaccinations per day? (variable Vac_perday)

6. How many missing values do we have for booster vaccinations? (variable Total_vacbooster)

7. What is the total number of vaccinations in row 14? (variable Total_vac)

Exercise 6: Checking data structure of a dataset – solution

# 1. Import the excel file Covid_19_vaccines_Basel into R
covid <- read_excel(file.path(d_oridata, "Covid19_vaccines_Basel.xlsx"))

# 2. How many columns does the dataset have?
# 6 columns
dim(covid)
ncol(covid)

# 3. How many rows?
# 915 rows
dim(covid)
nrow(covid)

# 4. Use one of the functions to get an overview of your data
str(covid) # option 1
glimpse(covid) # option 2

# 5. What is the median of vaccinations per day? (variable Vac_perday)
# 72 years
summary(covid)

# 6. How many missings do we have for booster vaccinations? (variable Total_vacbooster)
# 58
summary(covid)

# 7. What is the total number of vaccinations in line 14? (variable Total_vac)
# 15,806
head(covid, n = 14)

Inspect missing values

Check for missing values: Summarize missing values in the dataset

# Total missing values
sum(is.na(nhanes))

# Missing values per column
colSums(is.na(nhanes))

# Missing values for a specific variable
sum(is.na(nhanes$BMXWT))

Exercise 7: Inspect missing values

1. Check the number of missing values for all variables in the covid dataset.

2. Check missings only for total vaccine boosters (variable Total_vacbooster).

Exercise 7: Inspect missing values – solution

1. Check the number of missing values for all variables in the covid dataset.

2. Check missings only for total vaccine boosters (variable Total_vacbooster).

# 1. Check the number of missings for the whole covid dataset.
colSums(is.na(covid))

# 2. Check missings only for total vaccine boosters (variable Total_vacbooster).
# 684
sum(is.na(covid$Total_vacbooster))

The tidyverse

The tidyverse is a collection of R packages designed for data science.

Key Features: - Focus on tidy data principles.

• Easy-to-use, consistent syntax.

• Handles data manipulation, visualization, and more.

Core Packages:

• dplyr (data manipulation)

• ggplot2 (data visualization)

• tidyr (data tidying)

• readr (data import)

• tibble (modern data frames)

library(tidyverse)

Why Use the tidyverse?

• Simplifies Workflow: Combines common tasks (e.g., cleaning, analyzing, and visualizing data).

• Consistent Grammar: Shared principles across packages (e.g., “verbs” like filter, select, mutate in dplyr).

• Readable Code: Code becomes intuitive and easier to share or collaborate on.

• Built-in Visualization: ggplot2 helps create high-quality, customizable plots.

Comparison Base R and tidyverse

We want to:

1. Filter the rows for all who are males (RIAGENDR == 1)

2. Calculate the average weight.

Base R

mean(nhanes$BMXWT[nhanes$RIAGENDR == 1], na.rm = TRUE)

Tidyverse

nhanes %>%
      filter(RIAGENDR == 1) %>%
      summarize(mean_weight = mean(BMXWT, na.rm = TRUE))

Explanation tidyverse example

nhanes %>%
      filter(RIAGENDR == 1) %>%
      summarize(mean_weight = mean(BMXWT, na.rm = TRUE))

• nhanes %>%:Start with the nhanes dataset.

• filter(RIAGENDR == 1) %>%: Filter for “RIAGENDR == 1” (male), pass filtered dataset to next function.

• summarize(mean_weight = mean(BMXWT, na.rm = TRUE)): Calculate mean of BMXWT column and store it as mean_weight. na.rm = TRUE tells R to ignore/remove missing values for this operation.

The principle of piping

%>%:

1. Takes the output from the left: The value or object on the left side of the pipe is passed as the first argument of the function on the right side.

2. Sends it to the next step: After the function on the right finishes its work, its result is sent as input to the next function in the chain.

3. Repeat until done: This process continues for as many steps as you chain together.

Tidyverse goal for today

The most commonly used functions in the tidyverse (mostly from “dplyr”):

• select: For selecting columns

• filter: For filtering rows based upon condition(s).

• arrange: For sorting data

• rename: For renaming variables

• mutate: For creating / modifying variables.

• group_by and summarize: For aggregating data

• class conversions (e.g., as.factor): For converting a variable from one class into another one.

• if_else and case_if: For categorizing data

• relocate: For re-ordering variables in a dataframe

• relevel: For setting a reference category.

Select()

Selects specific columns from a dataset, to:

• reduce the number of columns in a dataset, making it easier to work with

• reorganize the order of columns

• exclude specific columns.

From now on, we will only work with a subset of variables from NHANES:

• SEQN: Respondent sequence number.

• RIAGENDR: Gender.

• RIDAGEYR: Age.

• BMXWT: Weight.

• BMXHT: Height.

Select() – Examples

Basic selection: This is the easiest way to choose your variables - selecting your variables by specific names. This is the dataset we will keep for other examples.

# Select specific columns
df <- nhanes %>%
      select(SEQN, RIAGENDR, RIDAGEYR, BMXWT, BMXHT)

# Check rows and columns
dim(df)

# Preview dataset
head(df)

Select() – Examples

Options: The select-function can do a lot more!

Reorder columns: You can reorder columns by specifying the order

# Reorder columns
dt<- nhanes %>%
  select(RIDAGEYR, BMXWT, BMXHT, SEQN, RIAGENDR) %>%
  head %>%
  print

Select() – Examples

Select columns by range: Use column positions to select columns

# Select columns by position
dn <- nhanes %>%
  select(1:3) %>%
  head %>%
  print

# Select consecutive columns using names - same result as above
dn <- nhanes %>%
      select(SEQN:RIDAGEYR)
head(dn)

Select() – Examples

Exclude columns using the “-” operator

# Exclude specific columns
dn <- nhanes %>%
  select(-SEQN:-INDHHIN2)

head(dn)

Select() – Examples

Select columns by pattern: You can select columns by pattern or name

# Select columns that start with "BMX"
dn <- nhanes %>%
  select(starts_with("BMX"))
head(dn)

# Select columns that contain "AGE"
dn <- nhanes %>%
  select(contains("AGE"))
head(dn)

# Select columns that end with "YR"
dn <- nhanes %>%
  select(ends_with("YR"))
head(dn)

Exercise 8: Select()

When writing the code for the following exercises, assign them to the object “ds”.

1. From nhanes, select the columns SEQN, RIAGENDR, and SMQ040.

2. Reorder the columns to: SMQ040, RIAGENDR, SEQN.

3. Exclude the BPXSY1 and BPXDI1 columns.

4. Select columns that start with BPX.

5. Bring two operations (1. and 2.) together using the pipe:

   a) Select only the columns SEQN, RIAGENDR, and SMQ040

   b) Reorder the columns to: SMQ040, RIAGENDR, SEQN

Exercise 8: Select() – solution

# 1. Select only the columns SEQN, RIAGENDR, and SMQ040.
ds <- nhanes %>%
            select(SEQN, RIAGENDR, SMQ040)

# 2. Reorder the columns to: SMQ040, RIAGENDR, SEQN.
ds <- nhanes %>%
            select(SMQ040, RIAGENDR, SEQN)

# 3. Exclude the BPXSY1 and BPXDI1 columns.
ds <- nhanes %>%
            select(-BPXSY1, -BPXDI1)

# 4. Select columns that start with "BPX".
ds <- nhanes %>%
            select(starts_with("BPX"))

# 5. Bring two operations together using the pipe
ds <- nhanes %>%
            # a) Select only the columns SEQN, RIAGENDR, and SMQ040 
            select(SEQN, RIAGENDR, SMQ040) %>%
            # b) Reorder the columns to: SMQ040, RIAGENDR, SEQN
            select(SMQ040, RIAGENDR, SEQN)

Filter()

filter(): select rows in a dataset that meet certain conditions, to:

• focus on relevant subsets of data for analysis.

• exclude rows that don’t meet certain criteria.

• explore and validate data by applying logical conditions.

Filter() – Examples

Basic filtering: Filter rows based on a single condition

# Filter for males only (RIAGENDR == 1)
dn <- df %>%
  filter(RIAGENDR == 1)

head(dn)

Filter() – Examples

If you work with variables of the class “factor”, you need to put the value (label) in quotation marks:

dn <- df %>%
      mutate(RIAGENDR = as.factor(RIAGENDR)) %>% # mutate to factor variable
      filter(RIAGENDR == "1")
head(dn)

Filter() – Examples

Filtering with multiple conditions: Use & for “AND” and | for “OR” to combine conditions

# Filter for females aged 30 or older
dn <- df %>%
  filter(RIAGENDR == 2 & RIDAGEYR >= 30) %>%
  summary %>%
  print

# Filter for individuals younger than 18 OR with weight above 80 kg
dn <- df %>%
  filter(RIDAGEYR > 18 | BMXWT > 80)

Filter() – Examples

Filtering for missing or non-missing data: To filter rows with missing values or exclude them

# Filter rows where weight is not missing
dn <- df %>%
  filter(!is.na(BMXWT)) %>%
  summary %>%
  print

# Filter rows where height is missing
dn <- df %>%
  filter(is.na(BMXHT)) %>%
  summary %>%
  print

Exercise 9: Filter

1. From nhanes select the following variables: SEQN, RIAGENDR, SMQ040, BPXSY1, BPXDI1. Assign them to the object “ds”.

   For the following exercises, use the dataset “ds” and assign any operations to the object “dt”.

2. Filter rows where RIAGENDR is 2 (female).

3. Filter rows where BPXSY1 (systolic blood pressure) is greater than 120 and BPXDI1 (diastolic blood pressure) is less than 80.

4. Combine multiple conditions with |: Filter rows where RIAGENDR is 1 (male) OR BPXSY1 is greater than 140.

Exercise 9: Filter – solution

# 1. Select the variables SEQN, RIAGENDR, SMQ040, BPXSY1, BPXDI1
ds <- nhanes %>%
            select(SEQN, RIAGENDR, SMQ040, BPXSY1, BPXDI1)

# 2. Filter rows for people who smoke everday (SMQ040 is 1).
dt <- ds %>%
  filter(SMQ040 == 1)

# 3. Filter rows where BPXSY1 (systolic blood pressure) is greater than 120
#    and BPXDI1 (diastolic blood pressure) is less than 80.
dt <- ds %>%
  filter(BPXSY1 > 140 & BPXDI1 > 90)

# 4. Combine multiple conditions with |:
#    Filter rows where RIAGENDR is 1 (male) OR BPXSY1 is greater than 140.
dt <- ds %>%
  filter(RIAGENDR == 1 | BPXSY1 > 140)

Arrange()

arrange() is used to reorder rows in a dataset based on the values in one or more columns. You can sort data in ascending (default) or descending order. You use arrange to:

• organize data for better readability

• identify the largest, smallest, or specific range of values

• prepare data for summary tables or reports

Arrange() – Examples

Basic sorting: Sort rows in ascending order by a single variable

# Sort by age (ascending order)
dn <- df %>%
  arrange(RIDAGEYR) %>%
  head %>%
  print

Arrange() – Examples

Sorting in descending order: Use desc() to sort rows in descending order

# Sort by weight (descending order)
dn <- df %>%
  arrange(desc(BMXWT))

head(dn)

Arrange() – Examples

Sorting by multiple variables: You can sort by multiple columns, specifying the order of precedence

# Sort by gender (ascending) and age (descending)
dn <- df %>%
  arrange(RIAGENDR, desc(RIDAGEYR))

head(dn)

Arrange() – Examples

Sorting with missing values: By default, arrange() places missing values (NA) at the end. You can also have them on top if needed.

# Sort by height with NA at the top
dn <- df %>%
  arrange(desc(is.na(BMXHT)))

head(dn)

Exercise 10: Arrange()

Use again your dataset “ds” and assign any operations to “dt”:

1. Sort the dataset by BPXSY1 (systolic blood pressure) in ascending order.

2. Sort the dataset by SMQ040 (smoking status) in descending order.

3. Filter rows where BPXDI1 (diastolic blood pressure) is greater than 90 and sort descending by BPXSY1.

Exercise 10: Arrange() – solution

# 1. Sort the dataset by BPXSY1 (systolic blood pressure) in ascending order.
dt <- ds %>%
  arrange(BPXSY1)

# 2. Sort the dataset by SMQ040 (smoking status) in descending order.
dt <- ds %>%
  arrange(desc(SMQ040))

# 3. Filter rows where BPXDI1 (diastolic blood pressure) is greater than 90 and
#    sort descending by BPXSY1.
dt <- ds %>%
  filter(BPXDI1 > 90) %>%
  arrange(desc(BPXSY1))

Rename()

rename() is used to rename variables in a dataset. It allows you to provide new, meaningful names to columns while preserving the dataset’s structure. You can use it to:

• To improve the readability and interpretability of your dataset.

• To standardize variable names for consistency in analysis.

• To simplify long or complex variable names.

Aftere the rename examples, we will keep working with the original variable names.

Rename() – Examples

Basic: Renaming one variable

# Rename a single variable
dn <- df %>%
      rename(gender = RIAGENDR)

# View the result
colnames(dn)

Rename() – Examples

Renaming multiple single variables at once

# Rename multiple variables
dn <- df %>%
    rename(
      ID = SEQN,
      gender = RIAGENDR,
      age = RIDAGEYR,
      weight = BMXWT,
      height = BMXHT
    )

# View the results
colnames(dn)

Rename() – Examples

Renaming variables based on patterns: Add a prefix, suffix, or replace variable names that have some common patterns in their name.

# Option 1: Add a prefix to all variables
di <- df %>%
  rename_with(~ paste0("NHANES_", .))

# View the result
colnames(di)

# Option 2: Add a suffix "_new" to variables starting with "BMX"
di <- df %>%
  rename_with(~ paste0(., "_new"), starts_with("BMX"))

# View the updated column names
colnames(di)

# Option 3: Replace "BMX" with "body" in variable names
di <- df %>%
  rename_with(~ sub("^BMX", "body", .), starts_with("BMX"))

# View the updated column names
colnames(di)

Rename() – Examples

Optional - Adding variable labels: If you also want to label your variables, you can use the labelled-package.

library(labelled)

# Assigning one variable label:
var_label(dn$gender) <- "Gender of participant"
# Check the label
var_label(dn$gender)

# You can also create a list and create multiple variable labels at once and assign them to a variabel:
var_label(dn) <- list(
  ID = "Participant id",
  gender = "Gender of participant",
  age = "Age at study",
  weight = "Weight in kg",
  height = "Height in kg"
)

# Check labels
var_label(dn)

Exercise 11: Rename()

Practice renaming variables using the ds-dataframe. Assign all operations to “dt”.

1. Rename BPXSY1 to systolicBP.

2. Rename SMQ040 to currentSmoker and BPXDI1 to diastolicBP.

3. Use rename_with() to replace BPX with pressure in all variables starting with BPX.

Exercise 11: Rename() – solution

# 1. Rename BPXSY1 to systolicBP.
dt <- ds %>%
      rename(systolicBP = BPXSY1)

# 2. Rename SMQ040 to currentSmoker and BPXDI1 to diastolicBP.
dt <- ds %>%
  rename(
    currentSmoker = SMQ040,
    diastolicBP = BPXDI1
  )

# 3. Use rename_with() to replace BPX with pressure in all variables starting with BPX.
dt <- ds %>%
  rename_with(~ sub("^BPX", "pressure", .), starts_with("BPX"))

Mutate()

mutate() is used to create new variables, modify existing ones, and perform calculations on existing data. You can use mutate to:

• derive new variables for analysis (e.g., calculate BMI)

• recode or categorize variables (e.g., age groups)

• perform transformations (e.g., converting units)

Mutate() – Examples

Creating new variables: Add a new variable. In this example, add the variable BMI (Body Mass Index: body weight in kg / (body height in m) ^2)

# Calculate BMI
dn <- df %>%
  mutate(BMI = BMXWT / (BMXHT / 100)^2)

# List column names
head(dn)

Mutate() – Examples

Modifying existing variables: In this example, we change age (currently in years) to months. Here, we overwrite the current age-variable. It is recommended to create a new variable

# Convert age to months
dn <- df %>%
  mutate(RIDAGEYR = RIDAGEYR * 12) %>%
  head %>%
  print

# Convert age to month and create a new variable
dn <- df %>%
  mutate(age_months = RIDAGEYR * 12) %>%
  relocate(age_months, .after = RIDAGEYR) %>% # this function moves one variable after another one
  head %>%
  print

Mutate() – Examples

Categorizing variables using if_else: Especially handy for binary categorizations. You can do more categories by nesting multiple if_else functions.

# Categorize age into two categories
df <- df %>%
   mutate(age_group_binary = if_else(RIDAGEYR >= 18, "adult", "child"))
head(dn)

# Categorize age into three categories
df <- df %>%
    mutate(age_group_three =
            if_else(RIDAGEYR < 13, "child", 
              if_else(RIDAGEYR >= 13 & RIDAGEYR < 18, "teenager", "adult")
            ))
head(df)

Mutate() – Examples

Categorizing variables using case_when: For categorizing variables, case_when is more flexibel for multiple conditions (> 2).

# Categorize BMI
df <- df %>%
  mutate(BMI = BMXWT / (BMXHT / 100)^2) %>%
  mutate(BMI_category = case_when(
    BMI < 18.5 ~ "underweight",
    BMI >= 18.5 & BMI < 25 ~ "normal",
    BMI >= 25 & BMI < 30 ~ "overweight",
    BMI >= 30 ~ "obese"
  ))

head(df, n = 20)

Exercise 12: Mutate()

Use again your dataset “ds” and assign all operations to this dataset (ds).

1. Create a new variable called pulse_pressure, calculated as BPXSY1 - BPXDI1.

2. Create a binary variables smoker_status using if_else():

   • 3 for SMQ040 (not at all), 1 otherwise (smoking everyday / some days).

3. Categorize BPXSY1 (systolic blood pressure) into three groups using case_when().

   • < 120: “normal”

   • 120-139: “elevated”

   • > 140: “hypertension”

Exercise 12: Mutate() – solution

# 1. Create Pulse_Pressure variable
ds <- ds %>%
  mutate(pulse_pressure = BPXSY1 - BPXDI1)

# 2. Create Smoker_Status using if_else()
ds <- ds %>%
  mutate(smoker_status = if_else(SMQ040 == 3, 0, 1))

# 3. Categorize systolic blood pressure using case_when()
ds <- ds %>%
  mutate(bp_category = case_when(
    BPXSY1 < 120 ~ "normal",
    BPXSY1 >= 120 & BPXSY1 < 140 ~ "elevated",
    BPXSY1 >= 140 ~ "hypertension"
  ))

Class conversions

Class conversions are essential to transform variable types. You use them to:

• prepare data for analysis (e.g., converting strings to factors for categorical variables).

• fixing data import issues (e.g., when numeric values are read as characters).

• customize variable types for specific functions (e.g., some models require factors).

Class conversions – Examples

String (character) to factor: To convert a character/string into a factor variable.

# Example: Convert BMI (character) to a factor
df <- df %>%
  mutate(BMI_category = as.factor(BMI_category))

# Check the class of the variable
class(df$BMI_category)
levels(df$BMI_category)

Class conversions – Examples

R orders the levels alphabetically if not specified otherwise. We can specify the level order using an adapted code:

df <- df %>%
        mutate(BMI_category = factor(BMI_category,
                                     levels = c("underweight", "normal", "overweight", "obese")))

# Check the class of the variable
class(df$BMI_category)
levels(df$BMI_category)


# Optional: If you want to set a different reference category, you can use the relevel() function
dn <- df %>%
        mutate(BMI_category = relevel(BMI_category, ref = "normal"))
levels(dn$BMI_category)

Class conversions – Examples

Numeric to factor: Useful for categorical variables stored as numbers.

# Example: Convert Gender (numeric) to a factor
dn <- df %>%
  mutate(RIAGENDR = as.factor(RIAGENDR))
class(dn$RIAGENDR)
levels(dn$RIAGENDR)

# Optionally, you can also assign value labels
df <- df %>%
      mutate(RIAGENDR = factor(RIAGENDR,
                               levels = c(1, 2),
                               labels = c("male", "female")))
# Check the result
class(df$RIAGENDR)
levels(df$RIAGENDR)
nlevels(df$RIAGENDR) # Gives you the number of levels

Class conversions – Examples

Factor to numeric: When you import datasets, it can happen that a numeric variable is recognized as a factor variable, which you then have to change:

df <- df %>%
  mutate(RIAGENDR_incorrect = as.numeric(RIAGENDR))
head(df)

# Drop again
df <- df %>%
  select(- RIAGENDR_incorrect)

Exercise 13: Class conversions

Work again with your dataframe “ds” and assign all operations to the dataframe “ds”.

1. Convert smoker_status from numeric to a factor with the following labels:

   • 0: “no smoker”

   • 1: “current smoker”

2. Convert the variable bp_category from string to factor variable. Order the levels the following: “normal”, “elevated”, “hypertension”.

Exercise 13: Class conversions – solution

# 1. Convert SMQ040 (smoking status) from numeric to a factor with the following labels:
ds <- ds %>%
  mutate(smoker_status = factor(smoker_status,
                        levels = c(0, 1),
                        labels = c("no smoker", "current smoker")))

# 2. Convert the variable bp_category from string to factor variable. Order the levels the following: "normal", "elevated", "high".
ds <- ds %>%
  mutate(bp_category = factor(bp_category,
                              levels = c("normal", "elevated", "hypertension")))

Exploring the data

Now that you prepared your dataset for analysis, we can run exploratory data analysis. We do this to uncover patterns, spot anomalies, and summarize its key characteristics. It usually involves:

• Descriptive statistics: Summarizing individual variables (e.g., mean and median)

• Visualizations: Exploring distributions and relationships between variables (e.g., boxplot, correlation matrix)

• Group comparisons: Comparing metrics across different categories (e.g., cross-tabulations, mean by group).

We will use different packages for exploratory data analysis including tidyverse, which you already know, and the packages “table1” and “summarytools”.

Simple descriptive statistics

We will calculate some basic descriptive statistics for numeric and factor variables. There are various ways of doing this in R, we will show you a couple of options with Base R, tidyverse, and summarytools.

Simple descriptive statistics: Numeric variables

Base R

# Summary function for an overview of a single variable
summary(df$RIDAGEYR, na.rm = FALSE)

# Individual calculations
min(df$RIDAGEYR, na.rm = TRUE)    # Minimum age
mean(df$RIDAGEYR, na.rm = TRUE)   # Mean age
median(df$RIDAGEYR, na.rm = TRUE) # Median age
max(df$RIDAGEYR, na.rm = TRUE)    # Maximum age

Simple descriptive statistics: Numeric variables

tidyverse

# calculate basic descriptive statistics for one variable
df %>%
  summarize(
    Min_Age = min(RIDAGEYR, na.rm = TRUE),
    Mean_Age = mean(RIDAGEYR, na.rm = TRUE),
    Median_Age = median(RIDAGEYR, na.rm = TRUE),
    Max_Age = max(RIDAGEYR, na.rm = TRUE)
  )

# calculate basic descriptive statistics for multiple variables
df %>%
  summarize(
    across(
      c(RIDAGEYR, BMXWT, BMXHT),
      list(
        Min = ~ min(.x, na.rm = TRUE),
        Mean = ~ mean(.x, na.rm = TRUE),
        Median = ~ median(.x, na.rm = TRUE),
        Max = ~ max(.x, na.rm = TRUE)
      ),
      .names = "{.col}_{.fn}"
    )
  )

# The .names argument controls how the new column names are generated:
# {.col} refers to the variable name (e.g., Variable1).
# {.fn} refers to the function name (e.g., Min, Mean, etc.).
# This ensures the output columns have clear and unique names.

Simple descriptive statistics: Numeric variables

summarytools

library(summarytools)

# Detailed descriptive statistics for RIDAGEYR (Age)
summarytools::descr(df$RIDAGEYR)

Simple descriptive statistics: Factor variables

Base R

# Frequency counts for gender
table(df$RIAGENDR)

# Proportions (relative frequencies)
prop.table(table(df$RIAGENDR))

Simple descriptive statistics: Factor variables

tidyverse

# Frequency counts for RIAGENDR (Gender)
df %>%
  count(RIAGENDR)

# Add proportions
df %>%
  count(RIAGENDR) %>%
  mutate(Proportion = n / sum(n)) #  # calculates the proportion of each category relative to the total count of observations

Simple descriptive statistics: Factor variables

summarytools

freq(df$RIAGENDR)

Simple descriptive statistics: Numeric & factor variables

If you want to create a more comprehensive overview of both numeric and factor variables, the packages summarytools and table 1 can be very helpful.

table1: This is a useful function to create an overview of various variable types, and is a great format that you can also export to a word document.

library(table1)

# Descriptives for age, gender, BMI, and BMI category
table1(~ RIAGENDR + RIDAGEYR + BMI + BMI_category,
        data = df)

# Descriptives and omitting missings for each variable
table1(~ RIAGENDR + RIDAGEYR + BMI + BMI_category,
        data = df,
        render.missing = NULL)

Simple descriptive statistics: Numeric & factor variable

summarytools: The function “dfSummary” creates a comprehensive overview of dataframe, including basic descriptive statistics, value codings, histogramms / bar plots, and missings.

# Summary of dataframe df
dfSummary(df)

# Summary of dataframe df as html-output
view(dfSummary(df))

Exercise 14: Simple descriptive statistics

Use your dataframe ds for the following exercises.

1. Calculate basic descriptive statistics (minimum, mean, median, and maximum) for the following numeric variables: BPXSY1 (systolic blood pressure), BPXDI1 (diastolic blood pressure).

2. Summarize frequencies and proportions for bp_category (blood pressure category) and smoker_status.

Exercise 14: Simple descriptive statistics – solution

Base R:

# 1. Calculate basic descriptive statistics (minimum, mean, median, and maximum) for the following numeric variables: BPXSY1 (systolic blood pressure), BPXDI1 (diastolic blood pressure).

# BPXSY1 (Systolic blood pressure)
summary(ds$BPXSY1)
min(ds$BPXSY1, na.rm = TRUE)
mean(ds$BPXSY1, na.rm = TRUE)
median(ds$BPXSY1, na.rm = TRUE)
max(ds$BPXSY1, na.rm = TRUE)

# BPXDI1 (Diastolic blood pressure)
summary(ds$BPXDI1)
min(ds$BPXDI1, na.rm = TRUE)
mean(ds$BPXDI1, na.rm = TRUE)
median(ds$BPXDI1, na.rm = TRUE)
max(ds$BPXDI1, na.rm = TRUE)

# 2. Summarize frequencies and proportions for bp_category (blood pressure category) and smoker_status.

# Frequency and proportions for bp_category
table(ds$bp_category)
prop.table(table(ds$bp_category))

# Frequency and proportions for smoker_status
table(ds$smoker_status)
prop.table(table(ds$smoker_status))

Exercise 14: Simple descriptive statistics – solution

Tidyverse:

# 1. Calculate basic descriptive statistics (minimum, mean, median, and maximum) for the following numeric variables: BPXSY1 (systolic blood pressure), BPXDI1 (diastolic blood pressure).

## tidyverse

ds %>% # specifying for each variable on its own
  summarize(
    Min_Systolic = min(BPXSY1, na.rm = TRUE),
    Mean_Systolic = mean(BPXSY1, na.rm = TRUE),
    Median_Systolic = median(BPXSY1, na.rm = TRUE),
    Max_Systolic = max(BPXSY1, na.rm = TRUE),
    Min_Diastolic = min(BPXDI1, na.rm = TRUE),
    Mean_Diastolic = mean(BPXDI1, na.rm = TRUE),
    Median_Diastolic = median(BPXDI1, na.rm = TRUE),
    Max_Diastolic = max(BPXDI1, na.rm = TRUE)
  )


ds %>% # using summarize across
  summarize(
      across(
        c(BPXSY1, BPXDI1),
          list(
            Min = ~ min(.x, na.rm = TRUE),
            Mean = ~ mean(.x, na.rm = TRUE),
            Median = ~median(.x, na.rm = TRUE),
            Max =  ~max(.x, na.rm = TRUE)
          ),
        .names = "{.col}_{.fn}")
     )

# 2. Summarize frequencies and proportions for bp_category (blood pressure category) and smoker_status.

## tidyverse

# Frequency and proportions for bp_category
ds %>%
  count(bp_category) %>%
  mutate(Proportion = n / sum(n))

# Frequency and proportions for smoker_status
ds %>%
  count(smoker_status) %>%
  mutate(Proportion = n / sum(n))

Exercise 14: Simple descriptive statistics – solution

Summarytools and table1:

# 1. Calculate basic descriptive statistics (minimum, mean, median, and maximum) for the following numeric variables: BPXSY1 (systolic blood pressure), BPXDI1 (diastolic blood pressure).

descr(ds$BPXSY1)
descr(ds$BPXDI1)

# 2. Summarize frequencies and proportions for bp_category (blood pressure category) and smoker_status.

freq(ds$bp_category)
freq(ds$smoker_status)

## Tasks 1 & 2 together

# using table1
table1(~ BPXSY1 + BPXDI1 + bp_category + smoker_status,    data = ds)

# using summarytools
dfSummary(ds)

Visual inspection

While descriptive statistics are helpful, any exploratory data analysis requires visual inspection to

• explore variable distributions

• assess the spread of data and outliers

• examine relationships between two numeric variables

• visualize categorical data

Note on visualizations: Here, we will use baseR for simplicity. However, tidyverse (ggplot2) can also be used.

Visualisation: Histograms

Histograms display the distribution of a numeric variable by dividing it into intervals (bins) and showing the frequency of observations in each interval.

# Basic version
hist(df$BMI)

# Plot with some specifications
hist(
  df$BMI,
  main = "Distribution of BMI",
  xlab = "BMI",
  col = "skyblue",
  border = "white"
)

Visualisation: Boxplots

Boxplots summarize the distribution of a numeric variable, highlighting median, quartiles, and potential outliers.

# Basic boxplot
boxplot(df$BMI)

# Boxplot stratified and adapted (BMI by age group)
boxplot(
  BMI ~ age_group_three,
  data = df,
  main = "BMI by Gender",
  xlab = "Age group",
  ylab = "BMI",
  col = c("lightblue", "lightgreen", "lightyellow")
)

Visualisation: Scatterplot

Scatterplots are used to explore relationships between two numeric variables.

# Basic scatterplot
plot(df$BMXHT, df$BMXWT)

# Scatterplot with some specifications
plot(
  df$BMXHT, df$BMXWT,
  main = "Scatterplot Height vs. Weight",
  xlab = "Height (cm)",
  ylab = "Weight (kg)",
  col = "darkgreen",
  pch = 19 # with this number you can specify the shape of the points
)

Visualisation: Bar charts

Bar charts are used to visualize frequencies of categorical variables.

# Basic bar chart
barplot(table(df$BMI_category))

# Bar chart with some specifications
barplot(table(df$BMI_category),
  main = "BMI Categories",
  xlab = "BMI Category",
  ylab = "Count",
  col = c("orange", "yellow", "red", "green")
)

Exercise 15: Visualizations

1. Create a histogram for BPXSY1 (systolic blood pressure).
2. Create a boxplot for BPXSY1 (diastolic blood pressure) and stratify by smoker_status. Title the figure “Blood pressure and smoker status”. Label the x-axis “Smoking status”, the y-axis “Systolic blood pressure”. Color the boxplots in blue and yellow.
3. Create a scatterplot showing the relationship between BPXSY1 (systolic blood pressure) and BPXDI1 (diastolic blood pressure).
4. Create a bar chart for bp_category (blood pressure category).

Exercise 15: Visualizations – solution

# 1. Create a histogram for BPXSY1 (systolic blood pressure).
hist(ds$BPXSY1)

# 2. Create a boxplot for BPXSY1 (diastolic blood pressure) and stratify by smoker_status. Title the figure "Blood pressure and smoker status". Label the x-axis "Smoking status", the y-axis "Systolic blood pressure". Color the boxplots in blue and yellow.
boxplot(
  BPXSY1 ~ smoker_status,
  data = ds,
  main = "Blood pressure and smoker status",
  xlab = "Smoker status",
  ylab = "Systolic blood pressure",
  col = c("blue", "yellow")
)

# 3. Create a scatterplot showing the relationship between BPXSY1 (systolic blood pressure) and BPXDI1 (diastolic blood pressure).
plot(ds$BPXSY1, ds$BPXDI1)

# 4. Create a bar chart for bp_category (blood pressure category).
barplot(table(ds$bp_category))

Descriptives by group

Sometimes you may want to explore descriptives separately for groups. We will go through

• grouping data by one or more categorical variables.

• calculate descriptive statistics (e.g., mean, median) for numeric variables within groups

• optionally include statistical tests for group differences

Descriptives by group: Examples

tidyverse and table1 are two helpful packages for calculating descriptives for each group.

tidyverse: In tidyverse, you can use the group_by() function to arrange data based upon a factor variable (e.g., age group), and to then calculate descriptives for each group.

# Grouped summaries for BMI and BMXWT by age group

df %>%
  group_by(age_group_three) %>%
  summarize(
    Mean_BMI = mean(BMI, na.rm = TRUE),
    Median_BMI = median(BMI, na.rm = TRUE),
    Mean_Weight = mean(BMXWT, na.rm = TRUE),
    Median_Weight = median(BMXWT, na.rm = TRUE),
    Count = n()
  )

Descriptives by group: Examples

table1: You can also use table 1 to stratify calculations by certain groups, using the “|” operator.

# Summary table for BMI and BMXWT grouped by age_group and with testing
table1(~ BMI + BMXWT | age_group_three,
       data = df)

Exercise 16: Descriptives by group

1. Use tidyverse to

   • filter for participants without missings for BPXSY1

   • group the dataset by bp_category

   • calculate the mean and median for BPXSY1 (systolic blood pressure)

2. Using table1, create a summary table for pulse_pressure stratified by RIAGENDR.

Exercise 16: Descriptives by group – solution

# 1. Use tidyverse to filter for participants without missings for BPXSY1, group the dataset by bp_category calculate the mean and median for BPXSY1 (systolic blood pressure)
ds %>%
  filter(!is.na(BPXSY1)) %>%
  group_by(bp_category) %>%
  summarize(
    mean_Systolic = mean(BPXSY1, na.rm = TRUE),
    median_Systolic = median(BPXSY1, na.rm = TRUE),
    count = n()
  )

# 2. Using table1, create a summary table for pulse_pressure stratified by RIAGENDR
table1(~ pulse_pressure | RIAGENDR,
       data = ds)

Cross-tabulations

To assess associations between categorical variables, you need contigency tables or cross-tabulations. To do those, you can again work with Base R, tidyverse, and summarytools.

Cross-tabulations: Examples

Base R

# Frequency table of age_group and BMI_category
table(df$age_group_three, df$BMI_category)

          
           underweight normal overweight obese
  adult            100   1382       1725  2227
  child           1262    448        101    44
  teenager          97    365        127   127

# Proportion table
prop.table(table(df$age_group_three, df$BMI_category), margin = 1)

          
           underweight     normal overweight      obese
  adult     0.01840265 0.25432462 0.31744571 0.40982702
  child     0.68032345 0.24150943 0.05444744 0.02371968
  teenager  0.13547486 0.50977654 0.17737430 0.17737430

# margin = 1 gives you row-wise proportions, margin = 2 column-wise proportions

Cross-tabulations: Examples

table1

# BMI category by age group
table1(~ BMI_category | age_group_three,
       render.missing = NULL, # take this out if you also want to see the missings
       data = df)

Cross-tabulations: Examples

summarytools

# BMI category by age group
ctable(df$BMI_category, df$age_group_three, prop = "r")

Cross-Tabulation, Row Proportions  
BMI_category * age_group_three  
Data Frame: df  

-------------- ----------------- -------------- -------------- ------------- ---------------
                 age_group_three          adult          child      teenager           Total
  BMI_category                                                                              
   underweight                      100 ( 6.9%)   1262 (86.5%)    97 ( 6.6%)   1459 (100.0%)
        normal                     1382 (63.0%)    448 (20.4%)   365 (16.6%)   2195 (100.0%)
    overweight                     1725 (88.3%)    101 ( 5.2%)   127 ( 6.5%)   1953 (100.0%)
         obese                     2227 (92.9%)     44 ( 1.8%)   127 ( 5.3%)   2398 (100.0%)
          <NA>                      422 (33.8%)    792 (63.4%)    35 ( 2.8%)   1249 (100.0%)
         Total                     5856 (63.3%)   2647 (28.6%)   751 ( 8.1%)   9254 (100.0%)
-------------- ----------------- -------------- -------------- ------------- ---------------

# You can also drop missing, use column percentages and add a chi-square test
ctable(df$BMI_category, df$age_group_three,
       useNA = "no",
       prop = "c",
       chisq = TRUE)

Cross-Tabulation, Column Proportions  
BMI_category * age_group_three  
Data Frame: df  


-------------- ----------------- --------------- --------------- -------------- ---------------
                 age_group_three           adult           child       teenager           Total
  BMI_category                                                                                 
   underweight                      100 (  1.8%)   1262 ( 68.0%)    97 ( 13.5%)   1459 ( 18.2%)
        normal                     1382 ( 25.4%)    448 ( 24.2%)   365 ( 51.0%)   2195 ( 27.4%)
    overweight                     1725 ( 31.7%)    101 (  5.4%)   127 ( 17.7%)   1953 ( 24.4%)
         obese                     2227 ( 41.0%)     44 (  2.4%)   127 ( 17.7%)   2398 ( 30.0%)
         Total                     5434 (100.0%)   1855 (100.0%)   716 (100.0%)   8005 (100.0%)
-------------- ----------------- --------------- --------------- -------------- ---------------

----------------------------
 Chi.squared   df   p.value 
------------- ---- ---------
  4627.584     6       0    
----------------------------

Exercise 17: Cross-tabulations

Using your dataset ds, * create a contigency table of bp_category and smoker_status.

• Calculate column-percentages, and run a chi-square test (remember to drop missings for the chi-square test).

Exercise 17: Cross-tabulations – solution

# Cross-tabulation with ctable of bp_category and smoker_status
ctable(ds$bp_category, ds$smoker_status, prop = "c", useNA = "no", chisq = TRUE)

Cross-Tabulation, Column Proportions  
bp_category * smoker_status  
Data Frame: ds  


-------------- --------------- --------------- ---------------- ---------------
                 smoker_status       no smoker   current smoker           Total
   bp_category                                                                 
        normal                    375 ( 33.3%)     359 ( 40.9%)    734 ( 36.6%)
      elevated                    439 ( 39.0%)     340 ( 38.8%)    779 ( 38.9%)
  hypertension                    313 ( 27.8%)     178 ( 20.3%)    491 ( 24.5%)
         Total                   1127 (100.0%)     877 (100.0%)   2004 (100.0%)
-------------- --------------- --------------- ---------------- ---------------

----------------------------
 Chi.squared   df   p.value 
------------- ---- ---------
   19.159      2     1e-04  
----------------------------